Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics

The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagno...

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Vydáno v:Journal of clinical medicine Ročník 10; číslo 14; s. 3100
Hlavní autoři: Yousefi, Bardia, Kawakita, Satoru, Amini, Arya, Akbari, Hamed, Advani, Shailesh M., Akhloufi, Moulay, Maldague, Xavier P. V., Ahadian, Samad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 14.07.2021
MDPI
Témata:
Accuracy
Artificial intelligence
Biomarkers
Clinical medicine
Coronaviruses
COVID-19
Disease transmission
Health care
Machine learning
Medical imaging
Neural networks
Pandemics
Pneumonia
Radiomics
Viral infections
imaging biomarker
chest X-ray imaging
deep latent space radiomics
COVID-19 detection
deep convolutional autoencoder (ConvAE)
2D U-Net model
deep-learning features
ISSN:2077-0383, 2077-0383
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Abstract The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson–Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4–74.4%) for multiclass and 89.6% (88.4–90.7%) for binary-class classification.
AbstractList The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson-Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4-74.4%) for multiclass and 89.6% (88.4-90.7%) for binary-class classification.
The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson-Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4-74.4%) for multiclass and 89.6% (88.4-90.7%) for binary-class classification.The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson-Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4-74.4%) for multiclass and 89.6% (88.4-90.7%) for binary-class classification.
Author Kawakita, Satoru
Akbari, Hamed
Advani, Shailesh M.
Yousefi, Bardia
Ahadian, Samad
Amini, Arya
Maldague, Xavier P. V.
Akhloufi, Moulay
AuthorAffiliation 3 Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA; aamini@coh.org
5 Department of Computer Science, Perception Robotics and Intelligent Machines (PRIME) Research Group, University of Moncton, New Brunswick, NB E1A 3E9, Canada; moulay.akhloufi@umoncton.ca
4 Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; AkbariHA@upenn.edu
2 Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; skawakita@terasaki.org (S.K.); sadvani@terasaki.org (S.M.A.)
1 Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
AuthorAffiliation_xml – name: 2 Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; skawakita@terasaki.org (S.K.); sadvani@terasaki.org (S.M.A.)
– name: 4 Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; AkbariHA@upenn.edu
– name: 1 Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
– name: 5 Department of Computer Science, Perception Robotics and Intelligent Machines (PRIME) Research Group, University of Moncton, New Brunswick, NB E1A 3E9, Canada; moulay.akhloufi@umoncton.ca
– name: 3 Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA; aamini@coh.org
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/34300266$$D View this record in MEDLINE/PubMed
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Issue 14
Keywords imaging biomarker
chest X-ray imaging
deep latent space radiomics
COVID-19 detection
deep convolutional autoencoder (ConvAE)
2D U-Net model
deep-learning features
Language English
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Snippet The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited...
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SubjectTerms Accuracy
Artificial intelligence
Biomarkers
Clinical medicine
Coronaviruses
COVID-19
Disease transmission
Health care
Machine learning
Medical imaging
Neural networks
Pandemics
Pneumonia
Radiomics
Viral infections
Title Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics
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Volume 10
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